scholarly journals Metal Roof Fault Diagnosis Method Based on RBF-SVM

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Liman Yang ◽  
Lianming Su ◽  
Yixuan Wang ◽  
Haifeng Jiang ◽  
Xueyao Yang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
Steel Structure ◽  
Industrial Buildings ◽  
Ultrasonic Ranging ◽  
Rbf Kernel ◽  
Property Losses ◽  
Low Efficiency ◽  
Bad Weather ◽  
Airport Terminals

Metal roof enclosure system is an important part of steel structure construction. In recent years, it has been widely used in large-scale public or industrial buildings such as stadiums, airport terminals, and convention centers. Affected by bad weather, various types of accidents on metal roofs frequently occurred, causing huge property losses and adverse effects. Because of wide span, long service life and hidden fault of metal roof, the manual inspection of metal roof has low efficiency, poor real-time performance, and it is difficult to find hidden faults. On the basis of summarizing the working principle of metal roof and cause of accidents, this paper classifies the fault types of metal roofs in detail and establishes a metal roof monitoring and fault diagnosis system using distributed multisource heterogeneous sensors and Zigbee wireless sensor networks. Monitoring data from strain gauge, laser ranging sensor, and ultrasonic ranging sensor is utilized comprehensively. By extracting time domain feature, the data trend characteristics and correlation characteristics are analyzed and fused to eliminate erroneous data and find superficial faults such as sensor drift and network interruption. Aiming to the hidden faults including plastic deformation and bolt looseness, an SVM fault diagnosis algorithm based on RBF kernel function is designed and applied to diagnose metal roof faults. The experimental results show that the RBF-SVM algorithm can achieve high classification accuracy.

scholarly journals Fault diagnosis of shield machine based on RFE and ELM

2020 ◽  
Author(s):  
Xuanyu Liu ◽  
Wenbo Qi ◽  
Yudong Wang ◽  
Cheng Shao ◽  
Qiumei Cong
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Large Scale ◽  
Engineering Application ◽  
Slow Speed ◽  
Multiple Systems ◽  
The Neural Network ◽  
Low Efficiency ◽  
Learning Machine ◽  
Shield Machine

Abstract Shield machine is a complex large-scale tunneling equipment with multiple systems and driving sources. In order to improve the accuracy and efficiency of fault diagnosis for shield machine, a method based on the combination of reverse feature elimination (RFE) and extreme learning machine (ELM) is proposed. For the characteristics of shield machine operation data with many dimensions and large quantity, the RFE method is introduced to reduce the dimension of data, eliminate the redundant dimension and remove the correlation between features. Considering the neural network has the slow speed and low efficiency of fault diagnosis, the ELM neural network classifier model is built based on the extremely learning mechanism for fault diagnosis of shield machine. The simulation results based on the field construction data show that this method improves the accuracy and efficiency of fault diagnosis of shield machine significantly and has good engineering application value.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

Curvature Variability Study for Small- and Large-Scale Linear Fresnel Solar Fields: A Step Toward Optimization

2017 ◽  
Vol 139 (5) ◽  
Author(s):  
Sara Benyakhlef ◽  
Ahmed Al Mers ◽  
Ossama Merroun ◽  
Abdelfattah Bouatem ◽  
Hamid Ajdad ◽  
...  
Keyword(s):  
Cost Reduction ◽  
Large Scale ◽  
Small Scale ◽  
Optimization Approach ◽  
Market Penetration ◽  
Concentrated Solar Power ◽  
Sustainable Technologies ◽  
Low Efficiency ◽  
New Research ◽  
Variability Study

Reducing levelized electricity costs of concentrated solar power (CSP) plants can be of great potential in accelerating the market penetration of these sustainable technologies. Linear Fresnel reflectors (LFRs) are one of these CSP technologies that may potentially contribute to such cost reduction. However, due to very little previous research, LFRs are considered as a low efficiency technology. In this type of solar collectors, there is a variety of design approaches when it comes to optimizing such systems. The present paper aims to tackle a new research axis based on variability study of heliostat curvature as an approach for optimizing small and large-scale LFRs. Numerical investigations based on a ray tracing model have demonstrated that LFR constructors should adopt a uniform curvature for small-scale LFRs and a variable curvature per row for large-scale LFRs. Better optical performances were obtained for LFRs regarding these adopted curvature types. An optimization approach based on the use of uniform heliostat curvature for small-scale LFRs has led to a system cost reduction by means of reducing its receiver surface and height.

scholarly journals Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
James Brinkhoff ◽  
Justin Vardanega ◽  
Andrew J. Robson
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Image ◽  
Resource Planning ◽  
Support Vector ◽  
Perennial Crop ◽  
Perennial Crops ◽  
Object Based ◽  
Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

scholarly journals Fault Diagnosis for Vehicle on-Board Equipments of High-Speed Railway Based on GA-ANFIS Model

2020 ◽  
Author(s):  
Linggang Kong ◽  
Shuo Li ◽  
Xinlong Chen ◽  
Hongyan Qin
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Principal Component ◽  
Weighting Factor ◽  
Anfis Model ◽  
High Speed Railway ◽  
Railway Line ◽  
Diagnosis Rate ◽  
Low Efficiency ◽  
Frequency Weighting

Vehicle on-board equipment is the most important train control equipment in high-speed railways. Due to the low efficiency and accuracy of manual detection, in this paper, we propose an intellectualized fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) network. Firstly, we collect the fault information sheets that are recorded by electrical personnel, using frequency weighting factor and principal component analysis (PCA) to realize the data extraction and dimension reduction; Then, in order to improve the fault diagnosis rate of the model, using genetic algorithm (GA) to optimize the parameters of the ANFIS network; Finally, using the fault data of a high-speed railway line in 2019 to test the model, the optimized ANFIS model can achieve 96% fault diagnosis rate for vehicle on-board equipments, which indicating the method is effective and accurate.

scholarly journals https://ijsea.com/archive/volume10/issue9/IJSEA10091005.pdf

2021 ◽  
Vol 10 (9) ◽  
pp. 144-147
Author(s):  
Huiling LI ◽  
Xuan SU ◽  
Shuaipeng ZHANG
Keyword(s):  
Process Model ◽  
Large Scale ◽  
Process Mining ◽  
Data Set ◽  
Systems Model ◽  
Event Logs ◽  
Event Log ◽  
Low Efficiency ◽  
Sampling Approach

Massive amounts of business process event logs are collected and stored by modern information systems. Model discovery aims to discover a process model from such event logs, however, most of the existing approaches still suffer from low efficiency when facing large-scale event logs. Event log sampling techniques provide an effective scheme to improve the efficiency of process discovery, but the existing techniques still cannot guarantee the quality of model mining. Therefore, a sampling approach based on set coverage algorithm named set coverage sampling approach is proposed. The proposed sampling approach has been implemented in the open-source process mining toolkit ProM. Furthermore, experiments using a real event log data set from conformance checking and time performance analysis show that the proposed event log sampling approach can greatly improve the efficiency of log sampling on the premise of ensuring the quality of model mining.

A multi-population state optimization algorithm for rail crack fault diagnosis

2021 ◽  
Author(s):  
Mengmeng Liu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
High Stability ◽  
Detection Method ◽  
Optimal Solutions ◽  
Complex Environments ◽  
Fast Convergence Rate ◽  
Low Efficiency

Abstract The rails usually work in complex environments, which makes them more prone to mechanical failures. In order to better diagnose the crack faults, a multi-population state optimization algorithm (MPVHGA) is proposed in this paper, which is used to solve the problems of low efficiency, easy precocity, and easy convergence of local optimal solutions in traditional genetic algorithms. The detection results of fault signals show that MPVHGA has the advantages of fast convergence rate, high stability, no stagnation, and no limitation of fixed iterations number. The average iterations number of MPVHGA in 100 independent iterations is about 1/5 of the traditional genetic algorithm (SGA for short) and about 1/3 of the population state optimization algorithm (VHGA for short), and the total convergence number of MPVHGA converges to 55 and 10 more than SGA and VHGA respectively, and the accuracy of fault diagnosis can reach 95.04%. On the basis of improving the performance of simple genetic algorithm, this paper provides a new detection method for rail crack fault diagnosis, which has important engineering practical value.

Stakeholder Approach for Quality Higher Education

2021 ◽  
pp. 1664-1690
Author(s):  
Neeta Baporikar
Keyword(s):  
Higher Education ◽  
Economic Growth ◽  
Growth And Development ◽  
Large Scale ◽  
Poor Quality ◽  
Stakeholder Approach ◽  
Higher Education System ◽  
Core Issue ◽  
Low Efficiency ◽  
Quality In Higher Education

Higher education is not necessary for economic growth and development is a general presumption, the belief being literacy and primary education is. Increased concerns for ‘Education for All', also led to overall neglect of higher education in many developing countries. But given the inter-dependence of one layer of education on the other, higher education becomes critically important for developing and sustaining a good quality primary and secondary education. It is also a necessary feature for economic growth, development and sustenance. Higher education system suffers from a yawning gap in funds, outdated regulatory mechanisms, poor quality, and low efficiency. Liberalization of sector to attract large scale investments is the key to access, affordability, and equity. However, the core issue still remains ‘quality in higher education'. This chapter through in depth literature review and content analysis delves into stakeholder approach for quality higher education, which would hopefully not only optimize the higher education impact but also guarantee quality higher education.

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